The book is well-written and entertaining. I would like to specifically point out 2 chapters that really stuck out to me. Chapter 4 on correlation provided an excellent description of why correlation is not the same as causation. It then went on to state that with all the data available, correlation might be all that is needed. Here is a quote from Chapter 4.

The correlations show what, not why, but as we have seen, knowing what is often good enough.

The final chapter, Chapter 10, is about what is next with big data. It provides a look into the future of where big data will make a difference: global problems, medicine, climate change, physics, sensors, and nearly all other parts of our lives. It also mentions that big data is only going to get bigger.

Also, chapter 5 introduced me to a new word, datafication. I am still not exactly sure what the definition is. Chapter 9 has a great discussion about privacy because people are losing control that information IS being collected. People can only hold others accountable for how the information is used.

Overall

The book will not help you master machine learning algorithms (it is not intended for that). It is not a technical book. However, if you are interested in what types of questions can be answered with all your data, this book is great. I believe the book is targeted at business people that are hoping to get a grasp of all the big data talk.

Like this:

As of yesterday, I was completely new to the term work-force science. Essentially, work-force science is data analysis applied to Human Resources. It makes more sense than the old gut feeling approach. If you want to know more, see this excellent article from the New York Times, Big Data, Trying to Build Better Workers. The following quote sums up one of the key findings.

An applicant’s work history is not a good predictor of future results.